From frozen cell bank to product assay: high-throughput strain characterisation for autonomous Design-Build-Test-Learn cycles

Background Modern genome editing enables rapid construction of genetic variants, which are further developed in Design-Build-Test-Learn cycles. To operate such cycles in high throughput, fully automated screening, including cultivation and analytics, is crucial in the Test phase. Here, we present the required steps to meet these demands, resulting in an automated microbioreactor platform that facilitates autonomous phenotyping from cryo culture to product assay. Results First, an automated deep freezer was integrated into the robotic platform to provide working cell banks at all times. A mobile cart allows flexible docking of the freezer to multiple platforms. Next, precultures were integrated within the microtiter plate for cultivation, resulting in highly reproducible main cultures as demonstrated for Corynebacterium glutamicum. To avoid manual exchange of microtiter plates after cultivation, two clean-in-place strategies were established and validated, resulting in restored sterile conditions within two hours. Combined with the previous steps, these changes enable a flexible start of experiments and greatly increase the walk-away time. Conclusions Overall, this work demonstrates the capability of our microbioreactor platform to perform autonomous, consecutive cultivation and phenotyping experiments. As highlighted in a case study of cutinase-secreting strains of C. glutamicum, the new procedure allows for flexible experimentation without human interaction while maintaining high reproducibility in early-stage screening processes. Supplementary Information The online version contains supplementary material available at 10.1186/s12934-023-02140-z.

: Batch times of cultures inoculated from cryo cultures stored at −80 • C and −20 • C for Escherichia coli. Experiments were conducted as described for Corynebacterium glutamicum in Section "Cell viability studies with C. glutamicum" of the main manuscript. Instead of CGXII medium, cultivation was performed in M9 medium modified from Sambrook et al., 1989, containing 10 g L −1 glucose, 0.001 g L −1 biotin and 20.93 g L −1 3-(morpholin-4-yl)propane-1-sulfonic acid (MOPS) buffer. Batch times were investigated weekly over the course of six weeks by cultivation and subsequent spline analysis of the growth curves. For each of the storage conditions and weeks, three different cryo cultures were used, each of those for inoculation of four wells to an optical density (OD) of 0.1. This leads to a number of 12 replicates per storage condition and week. A minimum error in time of 4 min was assumed since this is the cycle time used for measurements in the BioLector ® .
Week Batch time (-80 ℃)   13.63 ± 0.10 11.94 ± 0.07 10-hour reference 13.62 ± 0.08 11.83 ± 0.07 9-hour evaporation 13.81 ± 0.15 12.09 ± 0.08 9-hour reference 13.73 ± 0.10 12.09 ± 0.08 5-hour evaporation 12.60 ± 0.09 11.16 ± 0.07 5-hour reference 12.44 ± 0.07 11.05 ± 0.08 3-hour evaporation 13.77 ± 0.16 12.11 ± 0.16 3-hour reference 12.91 ± 0.17 11.37 ± 0.10 Figure S1: Comparison of evaporation times. Cultivation was performed at 30 • C, 1 400 rpm, 85% relative humidity in a BioLector ® Pro, using CGXII medium containing 10 g L −1 glucose. A FlowerPlate ® without optodes was used, sealed with a gas-permeable sealing foil with perforated silicone layer for automation. 24 out of 48 wells were filled with 800 µL methanol, which was subsequently removed in two steps as described in the main manuscript. Evaporation took place under the above-mentioned cultivation conditions. After the respective evaporation time, fresh CGXII medium was filled in all 48 wells and wells were inoculated with C. glutamicum wild-type to OD 0.1 or 0.2. Wells without methanol serve as reference. Evaporation times of 10 h, 9 h and 5 h all led to growth times that do not deviate from reference cultures that were not treated with methanol before inoculation. In contrast, 3 h is not sufficient to evaporate methanol, which can be seen in the slower growth. To guarantee a buffer even for sub-optimal liquid handling in a cultivation process, 10 h were used for clean-in-place (CIP). Shorter times were not further tested in the complete process since the CIP with medium led to even shorter process times. Batch times were calculated for comparison as shown in Table S2. Figure S2: Influence of residual methanol on growth behaviour. Cultivation of C. glutamicum wild-type was performed at 30 • C, 1 400 rpm, 85% relative humidity in a BioLector ® I, using CGXII medium containing 10 g L −1 glucose and various amounts of methanol. Per concentration, six replicates were cultivated in separated wells of a FlowerPlate ® without optodes. The initial OD was 0.1 for all replicates. Even small amounts of 0.125% methanol in CGXII medium, which corresponds to 1 µL in 800 µL cultivation medium, lead to prolonged batch times and a change in signal. This also means that insufficient evaporation times can be easily detected in the backscatter signal, as shown in Fig S1. Figure S3: Comparison of CIP with CGXII medium and untreated wells. Cultivation was performed at 30 • C, 1 400 rpm, 85% relative humidity in a BioLector ® Pro, using CGXII medium containing 10 g L −1 glucose. FlowerPlates ® sealed with a gas-permeable sealing foil with perforated silicone layer for automation were used. Reference wells were filled with 800 µL CGXII medium inoculated to OD 0.1. For medium wash, medium was filled and removed repetitively as described in the main manuscript. Untreated wells (orange) and wells with several steps of medium washing (green, purple) show highly comparable growth behaviour. Since the few wells that show slightly delayed growth were those untreated, this effect is more likely to be caused by pipetting errors. A higher amount of residual medium due to accumulation during washing would lead to dilution of the cells at inoculation and thus slower growth, which was not observed here. Due to shorter process times, two instead of three washing steps were thus used in the final CIP procedure. Table S3: Batch times of three consecutive batches with CIP using CGXII medium. The calculated batch times are referring to data in Figure 5 of the main manuscript. Batch times were analysed by spline approximation and using the maximum of the first derivative for each replicate as the beginning of the stationary phase. For precultures, 12 biological replicates were analysed. Each of these were used to inoculate three main cultures, resulting in 36 main culture replicates. The second batch is the same data shown in Figure 3 in the main script.

Batch time (preculture) [h] Batch time (main culture) [h] Batch 1
10.40 ± 0.08 8.48 ± 0.12 Batch 2 10.12 ± 0.14 8.48 ± 0.10 Batch 3 10.14 ± 0.10 8.54 ± 0.41 Mean 10.23 ± 0.17 8.50 ± 0.26 Figure S4: Exemplary spline analysis for batch time calculation. C. glutamicum wild-type cultures in 800 µL CGXII medium were inoculated to the stated OD. In one case, the CIP procedure with methanol was applied to wells before cultivation; in the other case, wells were not treated. Using this example, which shows that the CIP does not affect the batch times, it can be seen how the spline methodology is suited for batch time analysis. Univariate cubic smoothing splines (UCSS) were calculated with the bletl python package. The first derivative of the splines shows a clear peak at the entry of the stationary phase for C. glutamicum wild-type. This maximum was also used to calculate batch times for comparing different cultivation and CIP strategies throughout the paper. Figure S5: Front window cut-out.
The resealable cut-out in the front window is needed to dock the transfer station of the freezer to the robotic platform.
In the picture, the smaller door for automation purposes, located at the back of the freezer, is shown.  The backscatter data in this figure corresponds to strategy 2 shown in Figure 6 in the main manuscript, meaning the cultures were spread over three different batch cultivations. Although cutinase activities were significantly higher for replicate 3 across several signal peptides, e.g. Mpr, analysis of backscatter did not reveal these effects. In addition, LipA showed a lower activity in the assay for replicate 3, but no evidence for this can be seen in the backscatter of main culture 3. The batch effects might thus be caused either by experimental error in the activity assay or variations in WCBs that cannot be detected in growth, but only influence the amount or activity of cutinase.

Plasmid
Description Reference